Detection of Face Wearing Mask Based on AdaBoost and YCrCb

ABSTRACT

At the beginning of 2020, COVID-19 broke out and swept the world. Wearing masks remains an important means of preventing epidemics. Many scholars have developed and studied mask wearing detection based on YOLO algorithm, and have made some achievements. AdaBoost algorithm has the advantages of high precision and low complexity, and is also suitable for solving this problem. This paper uses OpenCV to propose a face detection algorithm based on AdaBoost. This algorithm is based on face detection, including initialization of background estimation example, background subtraction preprocessing, obtaining eye position, face detection and other steps. LBP features are used as the training basis of the classifier. The trained classifier is generated and used as a function in the mask detection algorithm. At present, there are two problems in the research of mask wearing detection first, only consider whether the tested object wears a mask, but not analyze the non-standard wearing of masks;Secondly, due to the influence of light and other external environments, the real-time detection effect of targets in complex scenes changes greatly. In view of the above problems, this paper adopts the following methods to solve them pre-processing the image to reduce noise, light spots and other external environmental interference;For the case that the mask is not standardized, the condition that the mask covers the nose and mouth shall be detected. Finally, the Adaboost algorithm for facial mask wearing detection is obtained. Experiments show that the algorithm has high adaptability, robustness and accuracy, and can be used to promote the development of epidemic prevention. © 2022 IEEE.